Open Set Detection: Dhamija [1]} formally studied the impact of open set setting on popular object detectors. They noticed that the state of the art object detectors often classify unknown classes with high confidence to seen classes. This is despite the fact that the detectors are explicitly trained with a background class [2]}, [3]}, [4]} and/or apply one-vs-rest classifiers to model each class [5]}, [6]}. A dedicated body of work [7]}, [8]}, [9]} focuses on developing measures of (spatial and semantic) uncertainty in object detectors to reject unknown classes. E.g., [7]}, [8]} uses Monte Carlo Dropout [12]} sampling in a SSD detector to obtain uncertainty estimates. These methods, however, cannot incrementally adapt their knowledge in a dynamic world.
